Nowadays, in transportation industry, most of the transportation such as passenger transportation, goods transportation and the like happens via road transport system. However, drivers of such transportation vehicles may feel drowsy due to reasons such as fatigue, lack of sleep, medication and the like. Drowsiness of the drivers is considered as one of the main reasons for major road accidents. The road accidents lead to heavy operating losses for the transportation industry.
Currently, there exist many systems to detect drowsiness and fatigue in the transportation vehicles. These systems may work based on face image detection, calculating eye lid closure, detecting position of eyes, detecting position of head and the like. Some of the existing techniques for detecting drowsiness may be based on human physiological phenomena. These techniques may be implemented in two ways, in which one way includes measuring changes in physiological signals of the driver such as brain waves, heart rate, eye blinking and the like. Another way may include measuring physical changes such as sagging posture of the driver, leaning of the driver's head, open and close states of eyes of the driver and the like. Though these techniques may provide accurate results, it may not be realistic since sensing electrodes may have to be attached directly onto the driver's body for the working of these techniques. Attaching sensing electrodes to the driver's body may annoy and distract the driver. Further, attaching sensing electrodes to the driver's body for a long time results in perspiration on the sensors that eventually diminishes their ability to monitor accurately.
Some other existing techniques monitor vehicle behavior based on steering wheel movement, accelerator, brake patterns, vehicle speed, lateral acceleration, lateral displacement and the like. However, these techniques of drowsiness detection are limited to vehicle type and driver conditions. The existing techniques can avoid the road accidents to some extent by alerting the driver in critical conditions. However, these techniques may not be able to perform efficiently in varying vehicle driving conditions like different speeds and off-road conditions, varying lighting intensities on the road and the like that complicate monitoring process of various states of eyes due to random movement of the driver towards and away from these systems installed in the vehicle under these conditions. Due to inefficient performance under the above mentioned conditions, these existing systems may provide false alarms that makes the driver uncomfortable.